PROJECT SUMMARY Tactile perception is an active, exploratory process that emerges through a cycle of sensory input driven by purposeful motion. Consider a baseball pitcher, stroking the seams of the ball, reorienting and refining the grip to deliver a wicked curveball. In nearly every sensorimotor action, our body and mind has been trained to efficiently execute precision movements to extract relevant tactile cues for the task at hand. The ability to execute these movements, receive tactile sensation, and construct sensorimotor perception is impaired in millions of Americans following stroke or spinal injury. Understanding neural circuit mechanisms that underlie sensorimotor learning and integration could guide creation of more effective treatments for these and other impairments, and thus have a major positive impact on public health. The immediate goals of this proposal are to develop new technologies to link the activity in sensorimotor circuits to purposeful motion, tactile sensation, and perception, and to apply these tools to identify how movement and these circuits are refined to identify relevant tactile features during skilled actions. A successful outcome of this research would generate fundamental knowledge of the neural code for touch, for how sensory and motor signals are integrated at the level of neural circuits, and of general mechanisms of cortical circuit refinement across learning of skillful behavior. Furthermore, this work would produce a suite of new technologies for observing and manipulating neural circuit dynamics in real-time that can be adapted to other sensory modalities (e.g. vision, hearing) and animal models of nearly any neuropsychiatric disorder. The overall scientific question we will address: how does the sensorimotor system learn to encode and identify task-relevant tactile features and filter out irrelevant features? We will train mice to identify the distance or angle of a pole where both features vary. We will quantify touch forces and the refinement of the motor program throughout learning using a novel motion tracking system. We will map activity patterns of identified cell-types in sensorimotor circuits of cortex across learning using calcium imaging and electrophysiology. We will build a system that translates observed sensorimotor signals into predicted patterns of neural activity that represent object features. We will deploy new optogenetic tools to perturb activity patterns in closed-loop with tactile exploration to identify activity patterns that drive sensorimotor perception, distinguish between models of circuit refinement, and test two theories of the functional consequences of learning on cortical dynamics.